Time to the patient-specific 1st violation V1, second violation V2 and third violation V3, total number of violations, total ICU time and WPB% values.
\r\n\tThe emphasis is on developing or modifying the available oral health diagnosis and preventive and corrective methods for children starting from newborn to pre-schoolers to school going and up to adolescence.
\r\n\tProfessionals involved in providing oral health care to children must keep themselves updated with the available and newer behaviour management and dental procedures and techniques that may begin with the first dental visit of the child write up to providing preventive and comprehensive treatment to the child and develop long-lasting good oral health habits.
\r\n\tThis book will provide an opportunity for various health professionals to share their expertise which may vary from providing various forms of oral health procedures to children at an individual and community level.
In optic nerve disease excluding glaucoma, mainly three ophthalmoscopic presentations of the optic disc can be encountered: an apparently normal optic disc, an atrophic or an edematous optic disc. Many documentation methods in optic neuropathy (ON) have been used: drawings; color, monochromatic and angiographic photographs.
\nThe optical coherence tomography (OCT), based on interferometry analysis, is a major achievement particularly for documentation of quantitative changes in the optic nerve head: it allows a measure of the retinal nerve fiber layer thickness (RNFL). This retinal layer is composed of non myelinated axons (myelination is generally posterior to the cribriform lamina). OCT can quantify a decrease in thickness due to the atrophy by axonal loss or increase in thickness related to edema. This review aims to illustrate the usefulness of this RNFL quantification in the exploration of optic nerve and anterior visual pathway diseases.
\nAn extensive review of the literature on applications of OCT in optic neuropathy was performed. The PubMed search engine was applied to the keywords "optic neuropathy", "optical coherence tomography" and "retinal nerve fiber layer". Studies exploring the various diseases of the optic nerve were classified according to the OCT results obtained at the initial visit. Glaucomatous neuropathy was excluded from this review. Characteristic diseases are illustrated by clinical case reports.
\nThe average RNFL thickness is the most common parameter encountered in all publications. It represents the average of all measured thickness values in a predefined annular zone adjacent to the optic disc. In some publications, the macular volume has also been evaluated.
\nThe change over time of the average RNFL thickness could be characterized by two different patterns:
\na normal RNFL thickness at the initial visit, followed by a gradual decrease: progressive RNFL decrease
an increased RNFL thickness at the initial visit.
Multiple sclerosis (MS) is the most common cause of optic neuropathy, therefore the evaluation of RNFL thickness in MS was the subject of numerous publications [1,2]
\nAt the initial stage of optic neuritis, RNFL thickness is within normal limits, after 2 months it decreases and stabilizes between 6 and 12 months [3,4] as highlighted in case report 1.
\n\n
A 35 year-old man was admitted for subacute left monocular impairment. Left orbital pain increased by ocular movements was reported. Visual acuity was 1.0 on the right eye and 0.4 in the left eye.
\nRetinal colour photography (case 1)
Ophthalmoscopy noted normal appearance of the right optic disc and slight left optic disc hyperhemia (figure 1). Acute optic neuritis was suspected. On the OCT, there is a slight RNFL swelling in its nasal superior part, however the mean thickness remains within normal limits (figure 2).
\nOCT slight RNFL swelling in the left eye (case 1)
The left visual field was abnormal with a nasal inferior defect, the right visual field was normal (figure 3).
\nVisual fields (case 1)
After stabilization of the affected eye following an episode of optic neuritis, average RNFL thickness is between 59.75 and 85.00 μm. In the contralateral eye, this parameter is between 82.73 and 99.80 μm (normal range: 102.90 - 111.10 μm) [5]. Thus, axonal loss is often present in both eyes even if symptomatic optic neuritis affects one eye only.
\n\n
A 54-year-old woman suffered from multiple sclerosis and had already a left demyelinating optic neuritis a few years ago. Flair-weighted axial magnetic resonance image shows periventricular plaques (figure 4).
\nBrain MRI scan (case 2)
Visual acuity was 1.0 in both eyes. Left visual field displayed a small inferior central defect.
\nVisual field (case 2)
On ophthalmoscopy, a left temporal disc pallor indicative of previous optic neuritis was noted (figure 6).
\nRetinal colour photography: left optic disc pallor (case 2)
OCT could confirm the decrease of the left temporal RNFL (figure 7).
\nOCT: temporal RNFL thinning (case 2)
However, the importance of RNFL loss depends on the severity of MS in which optic neuritis occurs.
\nIn the eye affected by inaugural optic neuritis, the RNFL thickness stabilizes at 58.10 microns, and at 101.20 microns in the contralateral eye.
If optic neuritis occurs in patients presenting with a relapsing form of MS, RNFL loss is greater in the affected eye, stabilizing at an average thickness of 48.20 μm whereas the contralateral eye remains unaffected, the thickness remains at a high average level of 103.70 μm.
In secondary progressive forms, characterized by a continuous evolution toward a severe neurological disability, axonal loss is even greater in the affected eye of optic neuritis (39.50 μm on average), but also affects the contralateral eye (83.40 μm on average) [6]. This RNFL loss predominates in the temporal quadrant [3] [7], a clinical model demonstrating the coexistence of axonal loss and demyelinating lesions in MS.
Other parameters have been evaluated with OCT in MS, especially a decrease in macular volume which is correlated with axonal loss [8] [9]. The relationship between central and peripheral macular thickness is an indicator of the evolution of the disease [10]. In addition, in MS, RNFL loss is considered to be a fairly accurate indicator of overall axonal loss, both ocular and extraocular. In the early course of the disease, there is a correlation between the decrease in RNFL thickness and neurological disability assessed by the Expanded Disability Status Scale (EDSS) [11] which represents the global axonal loss in MS. There is a relationship between RNFL loss and brain atrophy [12]. Thus RNFL thickness also appears as a reliable marker of disease severity.
\nFinally, some correlations between OCT parameters and morphological and functional data have been reported.
\nAt the initial stage of optic neuritis: when RNFL thickness is often normal, visual evoked potentials (VEP) (and MRI when it is rapidly accessible) is more efficient to objectify the optic nerve lesions (eliminating a pithiatism when there is a doubt). Thus, at the early stages of MS, OCT is less sensitive than VEP for detecting clinical and subclinical optic neuropathy [13].
At distance of the initial attack of optic neuritis
there is no correlation between RNFL thickness (a marker of axonal loss) and P100 latency (a marker of demyelination) [14]. In optic neuritis, the increase of cup/disc ratio is inversely proportional to the decrease in visual acuity and the decrease in RNFL thickness [15];
there is a correlation between RNFL thickness (morphological marker of axonal loss) and visual acuity (a loss of one line of visual acuity corresponds to an average decrease of 5,40 μm in thickness) [16], pattern electroretinogram or pupillary reflex (functional markers of axonal loss) [17, 18].
In Devic\'s syndrome, unlike MS, optic neuropathy is severe and is associated with spinal cord lesions without brain damage (figure 10). In OCT, the significant decrease in the thickness of the layer of ganglion fibers reflects an atrophy more severe and diffuse than that observed in MS, mainly in the upper and lower quadrants [19,20]. As in MS, there is a correlation between the retinal nerve fiber layer thickness and the overall neurological disability assessed by the EDSS [21]. OCT can thus provide morphological arguments in the differential diagnosis of Devic’s syndrome and MS. In a comparative study, the average thickness was 63.60 μm in Devic\'s syndrome, whereas it is 88.30 μm in MS (102.00 μm in the group of MS patients in an eye with no history of optic neuropathy) [22]. The average RNFL loss is 15 μm in a patient suffering from MS, whereas it is 39.00 μm in the case of Devic\'s syndrome [22].
\n\n
A 35-year-old woman had bilateral optic neuritis. Left visual acuity was 1.0. Despite anti-inflammatory treatment, right visual acuity had not recovered and remained very low (0.02).
\nRetinal colour photography (case 3)
Optic atrophy is observed in the right eye, there is an optic disc pallor in the left eye (figure 8)\n
\nOCT RNFL: loss in the right eye (case 3)
Important decrease of retinal fiber layer was found on the right OCT with an average thickness of 65 µm (figure 9).
\nBrain and Medullar MRI scan (case 3)
Transverse myelitis was found on the MRI scan (Figure 10) as part of Devic’s disease.
\nCompression of visual pathways caused by a pituitary adenoma induces axonal loss responsible for RNFL loss on time domain OCT. The importance of this axonal loss predicts visual acuity changes and visual field recovery after surgery for pituitary adenoma [23]. The postoperative recovery is significantly better if the preoperative thickness is greater than 85.00 μm [24].
\n\n
A 75 year-old man complained of chronic headache. Visual acuity was 0.8 on the right eye and 0.4 in the left one.
\nVisual field: temporal asymmetric defect in both eyes (case 4)
Visual fields showed temporal defects in particular in the left field (Figure 11).
\nBrain MRI scan (case 4)
Magnetic resonance imaging discovered a large pituitary tumor with chiasm compression (figure 12).
\nPreoperative average RNFL thickness attests of significant axonal loss in the temporal area.
\nOCT: temporal RNFL thinning (case 4)
In dominant optic atrophy (DOA), there is bilateral symmetric optic nerve pallor (Case 5) related to retinal ganglion cell death. The decrease in the RNFL thickness in OCT is dominant in the temporal part [25].
\n\n
A seven year old girl is referred for poor visual acuity (RE 20/40, LE 20/50) associated with bilateral optic disc atrophy (Figure 14).
\nRetinal colour photography (case 5): bilateral optic atrophy
On color vision testing there is a blue-yellow confusion axis (Figure 15).
\nFarnsworth 15HUE (case 5): yellow blue defect
On the time domain OCT, a major bilateral RNFL loss is noted (Figure 16).
\nOCT: RNFL loss in both eyes (case 5)
There is no compressive process on the brain MRI which only displays atrophic optic nerves (figure 17).
\nBrain MRI scan (case 5): bilateral thinning of the optic nerves
A dominant pattern was found upon family enquiry (Figure 18).
\nFamily enquiry (case 5)
In the authors\' clinical experience, OCT is also useful in the morphological evaluation of optic neuropathies encountered in other hereditary diseases such as recessive optic neuropathy and Wolfram\'s disease. However no reports on this subject could be found.
\nOCT can be useful for the follow-up of visual loss in patients with toxic optic neuropathy due to smoking, alcohol consumption or treatment for tuberculosis. In the early stages of toxic optic neuropathy, RNFL edema may be detected in some patients before permanent visual loss occurs. RNFL decrease consecutive to the withdrawal of the toxic agent can be monitored with OCT.
\n\n
A 48-year-old woman complained of progressive and painless visual loss. She had alcohol and tobacco addiction. She was deficient in B vitamins. Visual acuity was 0.3 in the right eye and 0.4 in the left eye. Color fundus photos showed pale optic discs without hemorrhage. Miliary drusens were also observed on macular areas (Figure 19).
\nRetinal colour photography (case 6): bilateral optic atrophy
Centrocaecal scotomas were visible in both visual fields (Figure 20)\n
\nVisual fields (case 6)
OCT confirmed optic nerve atrophy demonstrating a global RNFL loss.
\nOCT RNFL: loss in both eyes (case 6)
In optic disc swelling, an increase in the RNFL thickness can be quantified by OCT.
\nDepending on the underlying disease, the condition will either resolve with normalization or shift to optic atrophy with axonal loss. This evolution can be monitored with OCT.
\nOptic disc swelling due to idiopathic intracranial hypertension is referred to as papilledema [26]. It may be responsible for the deterioration of visual function and progression to optic atrophy. Visual field and VEP are prognostic indicators for visual outcome [27]. This is not the case for RNFL thickness changes which only enable to monitor the progression of the disease.
\n\n
A 28-year-old woman suffered from headache and vomiting. On ophthalmologic examination, visual acuity was 0.9 on the right eye and 1.0 on the left eye without oculomotor paralysis. This condition was due to intracranial hypertension. A bilateral papilloedema, venous engorgement, white exudates and superficial retinal folds was found (Figure 22).
\nRetinal colour photography (case 7): bilateral optic nerve head swelling
Blind spots were enlarged on visual fields (figure 23).
\nVisual fields (case 7)
After spinal puncture and acetazolamide medication, she recovered normal visual acuity. OCT follow-up enabled to monitor the improvement with treatment (Figure 24).
\nOCT RNFL follow-up (case 7)
At the initial stage of an anterior ischemic optic neuropathy, there is most often a progressive optic disc swelling. Then, axonal loss is responsible for a gradual reduction of RNFL leading to a variable stage of optic atrophy stabilizing at six months of onset [28].
\nAt the acute phase, Leber’s hereditary optic neuropathy associates peripapillary telangiectasia, tortuosity of retinal vessels and a peripapillary RNFL swelling with apparent optic disc swelling (figure 25-27). Within a few months (generally less than six months), diffuse optic atrophy occurs without excavation [29,30].
\n\n
An 11-year-old girl with no family history of Leber’s disease suddenly presented with severe painless central vision loss in the right eye. Dilated capillaries in the retina adjacent to the optic nerve head were found in both eyes. Two months after onset, visual acuity in the left eye also decreased.
\nRetinal colour photography (case 8)
Average RNFL thickness was increased in both eyes on the initial examination and it then slowly decreased as optic atrophy developed.
\nInitial OCT RNFL (case 8)
End stage OCT RNFL (case 8)
Many different diseases can be responsible for increased intracranial pressure such as infectious meningitis (case 9). As in other diseases with initial optic disc swelling, OCT enables to monitor RNFL thickness reduction after causal treatment.
\n\n
A 47-year-old man was admitted for visual field disorders with papulosquamous eruption of the palms and soles within two weeks following a previous asymptomatic general skin eruption. Visual acuity was 0.8 in both eyes. Bilateral optic nerve head swelling was observed (figure 28).
\nRetinal colour photography (case 9): Optic nerve head swelling
Goldmann visual fields displayed inferior altitudinal field defects (Figure 29).
\nVisual fields (case 9)
Serological tests on blood and cerebrospinal fluid confirmed a syphilitic infection. After initial intravenous penicillin G, the patient was discharged with ceftriaxone injections (1 g/day) for three weeks. On the OCT scans, the decrease of optic nerve head swelling was replaced with superior optic atrophy (figure 30).
\nOCT RNFL follow-up (case 9)
Although OCT provides very useful quantitative information on RNFL thickness, there are several limitation inherent to the device which should be highlighted.
\nIn the OCT database, normal values apply only to patients over 18 years. Evaluation of RNFL thickness is based on the assumption that there are no significant changes in normals from birth to the age of 18.
\nRNFL thickness evaluation by OCT is not a reliable method in case of major morphologic changes of the optic nerve head such peripapillary atrophy in high myopia, staphyloma or optic pits.
\nUltra-red light has to pass through the transparent media of the eye to reach the retina. In case of corneal dystrophy, cataract, vitreous opacity, there is a signal decrease and RNFL thickness measurements become less reliable.
\nAs in macular disease, OCT has become a precious tool that contributes to improve management of optic nerve disease. This review illustrates the many indications in neuro-ophthalmology, although there are limitations to its use. OCT measures RNFL thickness to assess axonal loss [31]. Axonal loss may be masked in cases with optic nerve head swelling due to the inhibition of orthograde axoplasmic transport at the initial stage of the disease.
\nDespite the high utility of OCT in neuro-ophthalmology, exclusive RNFL thickness analysis is not sufficient for assessing optic nerve disease. OCT results should always be interpreted in the light of clinical ophthalmoloscopy and visual function (visual acuity, perimetry, visual evoked potentials).
\nThe authors hereby declare that they have no conflict of interest related to this article.
\nPain management is increasingly recognised as a formal medical subspecialty worldwide [1]. Optimal sedation and analgesic strategies, combined with delirium management, are a challenge when caring for critically ill patients. Sedation in ICU aims to provide patient pain reflief, comfort and safety. For sedation monitoring, the most extensively used tools are RASS (Richmond Agitation and Sedation Scale) [2] and SAS (Sedation Agitation Scale) [3]. Despite extensive improvements in analgesia medication there are still barriers to nurses’ assessment, management, documentation, and reassessment of pain [4, 5, 6].
Pain is the most common reason that patients come to the emergency department. Emergency nurses have an indispensable role in the management of this pain [7, 8]. Sedation in the intensive care unit (ICU) is challenging, as both over- and under-sedation are detrimental. Current methods of assessment, such as the Richmond Agitation Sedation Scale (RASS), are measured intermittently and invariable depend on patients’ behavioural response to stimulation, as such may interrupt sleep and rest. A non-stimulating method for continuous sedation monitoring may be beneficial and allow more frequent assessment., noting that appropriate sedation cycling has to accommodate patients’ oscillations between states of agitation and over-sedation, which are detrimental to patient health and increases hospital length of stay [9, 10, 11, 12, 13, 14].
As such there also have been recent studies exploring the impact of augmenting sedation assessment with physiologic monitors [15] and studying the correlation between observational scales of sedation and bispectral index scores [16]. Recently the feasibility of continuous sedation monitoring of ICU patients using the NeuroSENSE was studied and suggested that such a non-stimulating method for continuous sedation monitoring may benefit patient care and allow increased A-S assessment [17]. The authors advocated use of incorporating some degree of automation into sedative drug administration, e.g. closed-loop control based on feedback from a processed EEG monitor, and various studies have suggested the limitations of RASS as a stand-alone measure of sedation levels, and pointed to benefit of adjunct continuous e.g., brain monitoring [17].
Earlier, Rudge, Chase, Shaw, Lee [12] discussed target controlled infusion (TCI) systems to deliver drugs to maintain target plasma concentrations, using a pharmacokinetic model, shown to be feasible when anaesthesia is given over short periods of reduced consciousness and well-known pharmacology is invoked. Infusion systems that regulate the infusion rate to maintain target agitation levels, to regulate the primary metric for longterm sedation, are one approach to improving care in the ICU. The data analysed in this chapter pertains to the scenario and data type studied earlier by [9, 10, 11, 12, 13, 14].
Assessing the severity of agitation is a challenging clinical problem as variability related to drug metabolism for each individual is often subjective. A multitude of previous studies suggest that the assessment accuracy of the sedation quality conducted by nurses tend to suffer from subjectivity and lead to sub-optimal sedation [14, 15, 18]. For example, [19] strongly recommend lighter than deeper levels of sedations. Moreover, [20, 21] argue that sedation should be reviewed and adjusted regularly. Whilst agitation management methods frequently rely on subjective agitation assessment [2, 3] the carers then select an appropriate infusion rate based upon their evaluation of these scales, experience, and intuition [21]. This approach usually leads to largely continuous infusions which lack a bolus-focused approach, commonly resulting in over or under-sedation. The work of [11, 12, 13] aimed to enhance feedback protocols for medical decision support systems and eventually automated sedation administration. A minimal differential equation model to predict or simulate each patient’s agitation-sedation status over time was presented in [12] for 37 ICU patients and was shown to capture patient A-S dynamics. The use of quantitative modelling to enhance understanding of the agitation-sedation (A-S) system and provision of an A-S simulation platform are one of the key tools in this area of patient critical care. A more refined A-S model, which utilised regression with an Epanechnikov kernel was formulated by [12]. A Bayesian approach using densities and wavelet shrinkage methods was later suggested by [9] to assess a previously derived deterministic, parametric A-S model [10, 11, 12, 13, 14], thus successfully challenging the practice of sedating ICU patients using continuous infusions. Wavelets approaches [9, 10] were shown to provide reliable diagnostics and visualisation tools to assess A-S models, giving alternative metrics of A-S control to assess validity of the earlier A-S deterministic models (Table 1 in [10]).
V1 | V2 | V3 | Total V’s | Time in ICU | WPB% | |
---|---|---|---|---|---|---|
P18/Good | 2 | 24 | 26 | 20 | 64 | 93.8% |
P28/Poor | 1 | 5 | 12 | 114 | 203 | 50.8% |
Time to the patient-specific 1st violation V1, second violation V2 and third violation V3, total number of violations, total ICU time and WPB% values.
This suite of wavelet metrics based on the discrete wavelet transform (DWT) were able to establish the value of earlier deterministic agitation-sedation (A-S) models against empirical (recorded) dynamic A-S infusion profiles, providing robust performance metrics of A-S control and excellent tools, as based on the classification of patients into poor and good trackers based on Wavelet Probability Bands (WPBs). Importantly, the WPBs were shown as a useful patient-specific method by which to identify and detect regions in the patient’s A-S profile i.e., times whilst in ICU, where the simulated infusion rate performs poorly, thus providing visual and quantified ways to help improve and distil the deterministic A-S model and in practice be a guage to alert carers.
In this chapter Empirical Transition Matrix (ETM) approach of multi-state counting process (survival analytic) models of Allignol and coauthors [22, 23], aligned with the counting process/event history work in [24, 25, 26], which use the times patients transition to varying states of violations (a violation being an A-S measure outside the 90% WPB bands), confirm the utility of defining trackers and non-trackers according to these control limits and wavelet diagnostic rules of Kang et al., [9, 10]. In this chapter ETM and multi-state modelling are found to be valuable for developing advanced optimal infusion controllers and also to assist coding of nurses A-S scores, which potentially offer significant clinical potential of improved agitation management and reduced length of stay, as an augmented approach to also using RASS and SAS. Establishing patient-specific thresholds of poor A-S management and control has significant implications for the effective administration of sedatives, as improved management of A-S states will allow clinicians to improve the efficacy of care and reduce healthcare costs [27, 28, 29].
This chapter models the agitation-sedation profiles of Agitation and Sedation (A-S) profiles of 37 patients were collected at the Christchurch Hospital, Christchurch School of Medicine and Health Sciences, NZ. Two measures were recorded for each patient: (1) the nurses’ ratings of a patient’s agitation level and (2) an automated sedation dose (see Figure 1). Infusion data were recorded using an electronic drug infusion device for all admitted ICU patients during a nine-month observation period and required more than 24 hours of sedation. Infusion data containing less than 48 hours of continuous data, or data from patients whose sedation requirements were extreme, such as those with severe head injuries, were excluded [9, 10]. A total of 37 ICU patients met these requirements and were enrolled in the study. Classification of patients into poor and good trackers, as based on the Wavelet Probability Bands (WPB) are given in Table 2. The so-called good tracker delineates the scenario where the nurse’s rating scores remains within the (time based) 90% coverage of wavelet probability band (WPB) based on the simulated dose profiles [9, 10]. Poor tracking delineates the scenario where the nurses rating scores remain outside the (time based) 90% coverage of wavelet probability band (WPB) for a significant portion of time based on the simulated dose profiles [13, 14].
Diagram of the feedback loop employing nursing staff’s feedback of subjectively assessed patient agitation through the infusion controller (diagram is sourced from Chase et al. [
By way of illustration we consider four patients from the pool of 37 patients.Tables 1 and 3 summarise each of these four patients’ WPB tracker status, time to first, second and third violation outside the WPB bands [9], their total number of violations over ICU stay and patient’s time in ICU, along with their specific WPB% value. Display of their line profiles of
V1 | V2 | V3 | Total V’s | Time in ICU | WPB% | |
---|---|---|---|---|---|---|
P8/Good | 1 | 2 | 3 | 46 | 128 | 87.5% |
P27/Poor | 1 | 4 | 5 | 89 | 225 | 43.7% |
Time to the patient-specific 1st violation V1, second violation V2 and third violation V3, total number of violations, total ICU time and WPB% values.
Line plot of nurses’ rating of patient agitation and the automated sedation dose for patient 8 (good tracker).
Line plot of nurses’ rating of patient agitation and the automated sedation dose for patient 27 (good tracker).
Line plot and WPB% band for patient 18 (LHS) and 28 (RHS, poor tracker).
The first patient (patient 8) in Table 3 is a good WPB tracker and the second a poor WPB tracker (patient 27), studied in depth in [28], for which upper tail thresholds of the nurses’ scores using copulas were established. We also refer the reader also to Hudson & Tursunalieva’s chapter in this book entitled “Copula thresholds and modelling Agitation-Sedation (A-S) in ICU: analysis of nurses scores of A-S and automated drug infusions by protocol” [27]. The corresponding WPB% values for patient 8 and patient 27 are 87.5% and 43.7%, respectively (Table 3). Overall, the minimum, median and maximum WPB% values for the 24 good trackers is (58.8%, 87.5%, 96.9%) and (47.3%, 64.8%, 77.3%) for the 13 poor trackers (Table 2). Noteworthy also is that the A-S time series of these two patients examined (P8 and P27) were of disparate lengths - patient eight had 10,561 time points and patient 27, 13,441 time points. The full 37 patients studied had a range of [3001–25,261] time points.
Patient 18 (good tracker) with a WPB% of 93.8% and patient 28 (poor tracker) with WPB% of 50.8% (Table 1) were studied in detail in [28], for which both upper and lower tails/thresholds of over or under-estimation of agitation levels by the nurses rating were established using copula dependence analytics [29], refer also to [27].
Patients vary according to their length of stay in ICU and consequently differ in their opportunity for violations to occur. The good trackers generally have shorter ICU time and thus less chance to exibit an increased total number of violations. An indication of how the strata (good versus poor tracker), the patient’s total number of violations and a patient’s time in ICU interact, can be visualied in Figure 5. The total number of WPB based violations is clearly greater for the poor trackers than for the good trackers, and it is the poor trackers that tend to have longer ICU times. Also from the scatterplot in Figure 5 there seems to be three approximate categories of patient ICU time: 50–64, 113–128 and 205–256. The majority of patients (28 (76%)) have ≤40 violations (RHS of Figure 5), 19 (51%) patients have an ICU time of ≤64 (Table 4).
Total number of violations by WPB tracking status [
ICU category | |||
---|---|---|---|
WPB tracker | 0 | 1 | 2 |
Good | 15 | 7 | 2 |
Poor | 4 | 4 | 5 |
Patient tracker status by ICU time: 0 = 50–64, 1 = 113–128, 2 = 205–256.
Accordingly, for ICU time categorised and coded as: 0 = [50,64], 1 = [113,128], and 2 = [205,256], the total violations profile according to tracking status, displayed in Figure 6, shows that the total number of violations is significantly higher for the poor trackers, particularly when ICU time > 205. Noteworthy, is that the majority of patients 28 (76%) have ≤40 violations (RHS of Figure 6), whereas 19 (51%) patients have an ICU time ≤ 64 (Table 4). Figure 7 displays the histogram of the number of violations where a violation is defined as a nurse’s A-S rating outside the patient’s WPB control band. We note that the majority of the time, in excess of >75% of the 370 violation counts are below a count of five violations (Figure 7).
Total number of violations by 3 levels of ICU time (LHS) and boxplot of poor good tracker time to 3rd violation by ICU time: 0 = [50,64], 1 = [113,128], 2 = [205,256].
Violation counts in bins across all patients.
For the state-space analysis described in Section 3 each patient’s total ICU time is broken into 10 bins, where each bin represents 10% of the patients’ total time in ICU; i.e., Bin 1: 0–10%, Bin 2: 11–20% etc. For the 37 patients, we thus have 370 bins, i.e. 370 counts of violations. The 10% interval approach is used due to the large variation in time in ICU between the WPB-based good versus poor strata [9] - noting that some poor trackers have times up to 256, whereas good trackers are mostly limited to 64–128. Given these bins, patients’ A-S states can then be defined in terms of the total number of violations or jumps outside the WPB bands that occur during each 10% interval of a patient’s total ICU time. The randon, outcome event of A-S status is then the number of violations that over time.
Multi-state models are known to provide a relevant framework for modelling complex event histories. Quantities of interest are mainly the transition probabilities that can be estimated by the empirical transition matrix, that is also referred to as the Aalen-Johansen estimator [30, 31]. Such multi-state models have had a wide range of applications for modelling complex courses of a disease over the course of time and across applications in medical research (Beyersmann et al., 2011 [32], Munoz-Price et al., 2016, [33], Andersen & Keiding 2002 [34]). We now utilise the Empirical Transition Matrix (etm) approach to model multi-state models of [22] and derive inference tests for such models using the approach of Commenges [25, 35, 36] with a particular focus on Commenges’ test derived in earlier work [25].
Define patient states as follows, any state can transition into any other state (Figure 8).
State-space system.
• State 1: 0 or 1 violations
• State 2: 2 or 3 violations
• State 3: > 3 violations
A number of different approaches (etm on ICU time, etm on bin time, and log-rank type tests as in Commenges [25], will be used to investigate the difference between good and poor trackers in terms of a devised a 3 state transition formulation as defined below. Differences between transition probabilities between states for the good and poor trackers will be evaluated using Commenges’ [25] chi square test.
The mathematics is well described in the work of Allignol [22], adapted to more complex scenarios in [23]; and in Commenges’ approach [25, 35, 36]. The mathematical formulation of the ETM state-space approach and Commenges’ test are given for general frameworks as follows.
Consider a stochastic process
The cumulative hazard transitions are defined as,
Define
as the probability that an individual who is in state
The (K + 1)x(K + 1) probability transition matrix with elements
and the diagonal elements
where the product is taken over all possible transition times in time interval (s,t].
An estimator for the covariance of the empirical transition matrix is given by,
which is a (K + 1)2 x (K + 1)2 matrix, with number or rows and columns equal (K + 1)2.
We now utilise the framework of the generalised Cochran–Mantel–Haenszel (CMH) test for (I x J x K) tables. The CMH test is based on the hypergeometric distribution. The CMH and the test of Commenges’ for the specific case here, where we have three states (of violations) and two strata (good versus poor trackers) is now described.
In our application then we have 2 × 3 × 3 tables. For each k = 1, 2, 3 we have a 2 × 3 table, where the elements are counts nijk (k denotes the departure state j, so the rows, I, are the WPB strata, the columns, J, are the entry states I and the slices, K, are the departure states j), as tabulated below.
I = 0 | I = 1 | I = 2 | Row total | |
---|---|---|---|---|
Good | n11k | n12k | n13k | n1 + k |
Poor | n21k | n22k | n23k | n2 + k |
Total | n+1k | n+2k | n+3k | n++k |
Assume that the row and column marginals are fixed. This implies that there are (I-1) by (J-1) values that are free to vary. For the cell nijk the expected value is then given by ni + k x n+jk. We are able to express all cell counts by the vector
where
The generalised CMH statistic is then given by,
which follows a chi square distribution with (I-1) by (J-1) degrees of freedom. This test is implemented in R via the mantelhaen.test function. Commenges [25] adapts this concept, with a test which differs to the generalised CMH test in that it does not sum over the K strata, before calculating the relevant chi squared test statistic.
Commenges’ test [25] is as follows,
where
The required total chi squared statistic is then simply obtained by taking
Conditionally on the number of patients in each state at each step we have 3 x 9 = 27 independent contingency tables (i.e., number of departure states j by the number of time points k–1, recall we have 10, 10% bins for a patient’s time in ICU) and each of these tables has dimension 2 × 3 (good/poor tracker by the number of states i).
The corresponding three specific strata tables are given in Tables 5–7. For example, for departure state j = 0 and time point k = 2 we have a two by three contingency table with an overall total of 7ϕ violations (labelled ϕ in Table 5); the latter informs that, at time k = 2 there are 5
Strata | I = 0 | I = 1 | I = 2 | Total | |
---|---|---|---|---|---|
k = 2 | Good | 5ϕ | 0 | 0 | 5 |
Poor | 0 | 2ϕ | 0 | 2 | |
Total | 5 | 2 | 0 | 7ϕ | |
k = 3 | Good | 10 | 5 | 1 | 16 |
Poor | 0 | 1 | 0 | 1 | |
Total | 10 | 6 | 1 | 17 | |
k = 4 | Good | 10 | 1 | 0 | 11 |
Poor | 2 | 0 | 0 | 2 | |
Total | 12 | 1 | 0 | 13 | |
k = 5 | Good | 9 | 2 | 2 | 13 |
Poor | 0 | 3 | 1 | 4 | |
Total | 9 | 5 | 3 | 17 | |
k = 6 | Good | 9 | 3 | 2 | 14 |
Poor | 0 | 0 | 0 | 0 | |
Total | 9 | 3 | 2 | 14 | |
k = 7 | Good | 9 | 2 | 2 | 13 |
Poor | 1 | 1 | 0 | 2 | |
Total | 10 | 3 | 2 | 15 | |
k = 8 | Good | 5 | 4 | 2 | 11 |
Poor | 2 | 0 | 2 | 4 | |
Total | 7 | 4 | 4 | 15 | |
k = 9 | Good | 5 | 2 | 0 | 7 |
Poor | 3 | 0 | 0 | 3 | |
Total | 8 | 2 | 0 | 10 | |
k = 10 | Good | 9 | 1 | 1 | 11 |
Poor | 2 | 1 | 2 | 5 | |
Total | 11 | 2 | 3 | 16 |
Departure state j = 0: WPB strata [9].
Strata | I = 0 | I = 1 | I = 2 | Total | |
---|---|---|---|---|---|
k = 2 | Good | 6 | 1 | 1 | 8 |
Poor | 1 | 1 | 0 | 2 | |
Total | 7 | 2 | 1 | 10 | |
k = 3 | Good | 1 | 2 | 0 | 3 |
Poor | 2 | 2 | 2 | 6 | |
Total | 3 | 4 | 2 | 9 | |
k = 4 | Good | 2 | 2 | 3 | 7 |
Poor | 2 | 2 | 0 | 4 | |
Total | 4 | 4 | 3 | 11 | |
k = 5 | Good | 3 | 1 | 1 | 5 |
Poor | 0 | 1 | 2 | 3 | |
Total | 3 | 2 | 3 | 8 | |
k = 6 | Good | 3 | 1 | 0 | 4 |
Poor | 1 | 3 | 1 | 5 | |
Total | 4 | 4 | 1 | 9 | |
k = 7 | Good | 2 | 3 | 1 | 6 |
Poor | 3 | 2 | 0 | 5 | |
Total | 5 | 5 | 1 | 11 | |
k = 8 | Good | 2 | 2 | 3 | 7 |
Poor | 1 | 1 | 2 | 4 | |
Total | 3 | 3 | 5 | 11 | |
k = 9 | Good | 4 | 3 | 2 | 9 |
Poor | 0 | 1 | 0 | 1 | |
Total | 4 | 4 | 2 | 10 | |
k = 10 | Good | 4 | 1 | 2 | 7 |
Poor | 1 | 1 | 0 | 2 | |
Total | 5 | 2 | 2 | 9 |
Departure state j = 1: WPB strata [9].
Strata | I = 0 | I = 1 | I = 2 | Total | |
---|---|---|---|---|---|
k = 2 | Good | 5 | 2 | 4 | 11 |
Poor | 0 | 3 | 6 | 9 | |
Total | 5 | 5 | 10 | 20 | |
k = 3 | Good | 0 | 0 | 5 | 5 |
Poor | 0 | 1 | 5 | 6 | |
Total | 0 | 1 | 10 | 11 | |
k = 4 | Good | 1 | 2 | 3 | 6 |
Poor | 0 | 1 | 6 | 7 | |
Total | 1 | 3 | 9 | 13 | |
k = 5 | Good | 2 | 1 | 3 | 6 |
Poor | 0 | 1 | 5 | 6 | |
Total | 2 | 2 | 8 | 12 | |
k = 6 | Good | 1 | 2 | 3 | 6 |
Poor | 1 | 2 | 5 | 8 | |
Total | 2 | 4 | 8 | 14 | |
k = 7 | Good | 0 | 2 | 3 | 5 |
Poor | 0 | 1 | 5 | 6 | |
Total | 0 | 3 | 8 | 11 | |
k = 8 | Good | 0 | 3 | 3 | 6 |
Poor | 0 | 0 | 5 | 5 | |
Total | 0 | 3 | 8 | 11 | |
k = 9 | Good | 2 | 2 | 4 | 8 |
Poor | 2 | 1 | 6 | 9 | |
Total | 4 | 3 | 10 | 17 | |
k = 10 | Good | 2 | 1 | 3 | 6 |
Poor | 1 | 0 | 5 | 6 | |
Total | 3 | 1 | 8 | 12 |
Departure state j = 2: WPB strata [9].
Three 2 x 3 contingency tables (one for each departure state j) are thus created.
Estimated transition probabilities for the 3 state process are then plotted using the ‘xyplot’ function from the lattice package in R. In the resultant plots (Figures 9–11), the vertical y-axis represents the transition probability value, which is represented by the solid line in each plot region. The numbers in the coloured bar above each plot defines the transition (e.g., 1 2 means transition probability from state 1 to state 2). The dotted lines around the solid line represent the confidence bands based on the covariance as calculated by the etm function. The horizontal x-axis shows the the time i.e., 10% bins (i.e. 2–10, because no transitions occur at time one being the initial state). For each tracker status and possible piairs of state transitions there are three plots, given in the following order, good trackers, poor trackers. Figure 11 displays the probability of being in each of the 3 states (0, 1, 2) given the initial state is state 0.
Transition probability profiles for
Transition probability profiles for
Probability of being in each state as time progresses given start state 0. Top is good trackers, bottom is poor trackers:
Our procedure results in three 2 x 3 contingency tables (one for each departure state j), see Tables 8–10. The chi squared statistic as derived in [25] can now be calculated in that for the Commenges test the same chi squared calculation is made for each state specific table separately (i.e., without summing over k). The results in this case are χ2(1) = 6.046, χ2 (2) = 2.269 and χ2(3) = 9.280. Each of these follows a chi square distribution with 2 degrees of freedom with associated p-values of 0.049, 0.322 and 0.010 (Table 11). Kang WPB (2013) [9].
Tracker strata | I = 0 | I = 1 | I = 2 | Total |
---|---|---|---|---|
Good | 71 | 20 | 10 | 101 |
Poor | 10 | 8 | 5 | 23 |
Total | 81 | 28 | 15 | 124 |
Departure state j = 0 summed over all time points k, k = 2, …,10.
Tracker strata | I = 0 | I = 1 | I = 2 | Total |
---|---|---|---|---|
Good | 27 | 16 | 13 | 56 |
Poor | 11 | 14 | 7 | 32 |
Total | 38 | 30 | 20 | 88 |
Departure state j = 1 summed over all time points k, k = 2, …,10.
Tracker strata | I = 0 | I = 1 | I = 2 | Total |
---|---|---|---|---|
Good | 13 | 15 | 31 | 59 |
Poor | 4 | 10 | 48 | 62 |
Total | 17 | 25 | 79 | 121 |
Departure state j = 2 summed over all time points k, k = 2, …,10.
State | χ2 | p-value |
---|---|---|
J = 0 | 6.046 | |
J = 1 | 2.269 | 0.322 |
J = 2 | 9.280 | |
Total |
Computation of Commenges’ test for the WPB strata [9].
Summing these threeχ 2 (j), j = 1,2,3 statistics gives a value of χ2 =17.59 with 6 degrees of freedom and an associated p-value of 0.007 (Table 11). The underlying null hypothesis is that the two nominal variables (strata: good or poor tracker and entry state: 0, 1, or 2) are conditionally independent in each stratum (departure state j; 0, 1 or 2), assuming no three-way interaction. The low p-value of 0.007 suggests that this hypothesis be rejected, i.e., the two variables are not conditionally independent. Thus the Commenges test shows that there is a statistically significant difference between the good versus poor tracker WPB strata, and that this difference is mainly due to transitions out of states 0 and 2, which agrees with the trends based on a graphical inspection of Figures 9–11.
The same procedure and related Commenges’ test is then applied to each of the 3 remaining good/poor tracker definitions of Kang [10, 11, 14] for the three-state context studied in this chapter. These results are reported in Table 12.
State | χ2 | p-value |
---|---|---|
J = 0 | 1.724 | 0.422 |
J = 1 | 0.911 | 0.634 |
J = 2 | 7.123 | |
Total | 0.135 |
Computation of Commenges’ test for the remaining A-S studies.
Kang et al., WCORR [10].
Chase et al. [14].
State | χ2 | p-value |
---|---|---|
J = 0 | 5.669 | 0.059 |
J = 1 | 6.406 | |
J = 2 | 3.097 | 0.213 |
Total |
Rudge et al. [11].
State | χ2 | p-value |
---|---|---|
J = 0 | 0.367 | 0.832 |
J = 1 | 2.951 | 0.229 |
J = 2 | 5.466 | 0.065* |
Total | 0.186 |
Transition probability profiles of being in each state as time progresses, given start state 0, for the remaining 3 studies of [10, 14, 11] are given in Figures 12–14. In summary, Figures 9–14 illustrate the trend that good trackers tend to have higher probability of transitioning into state 0 than poor trackers, and the good trackers tend to have lower probability of transitioning into state two than poor trackers, where state two indicates that more violations (>3 violations) are occurring, and state 0 indicates few violations are occurring.
Probability of being in each state as time progresses given start state 0. Top is good trackers; bottom is poor trackers: WCORR of [
Probability of being in each state as time progresses given start state 0. Top is good trackers; bottom is poor trackers according to Chase et al. [
Probability of being in each state as time progresses given start state 0. Top is good trackers; bottom is poor trackers according to Rudge et al. [
Notably also, the probability of transitioning into state 2 overall appears to increase as ICU time increases. This is most likely because poor tracking patients tend to have longer ICU times, and so, as time goes on, it is only poor trackers transitions that are being estimated. By categorising patients according to total ICU time (≤64, >64) as discussed earlier (Figures 5 and 6, Table 4) some of this could be accounted for. The results obtained are still consistent, as shown in the etm profiles using ICU time (≤64, >64) in Figures 15 and 16, respectively. The corresponding ETM probabilities are determined according to etm in R [21] and associated state and strata specific plots given in Figures 15 and 16.
Transition probability profiles for patients with ICU time ≤ 64. Top panel are the WPB good trackers, and bottom panel the poor trackers.
Transition probability profiles for patients with ICU time > 64. Top panel are the WPB good trackers, and bottom panel the poor trackers.
The different approaches in Section 3 led to the sasimilar conclusions that there is a difference in the way good trackers and poor trackers transition between states. Most of this difference occurs in states 0 and states 2, as defined. Good trackers tend to have higher probability of transitioning into state 0 than poor trackers, and good trackers tend to have lower probability of transitioning into state 2 than poor trackers, noting that state 2 indicates more violations are occurring, and state 0 indicates fewer violations. The probability of transitioning into state 2 overall appears to increase as ICU time increases. This is most likely due to the fact that poor tracking patients have longer ICU times, and so, as time goes on, it is only the poor trackers’ transitions that are being estimated.
By categorising patients according to their total ICU time (≤64, >64) similar trends were found. The Commenges’ test established a statistically significant difference between the two tracking strata (p = 0.007), and that this difference was mainly due to transitions out of states 0 and 2. For the tracking metric of Chase et al. [14], the Commenges test demonstrated a statistically significant difference between the two good versus poor strata (p = 0.019), with this difference mainly due to transitions out of states 0 and 1. Overall, the WCORR [10] and Rudge [11] classifications of tracking/strata, the transision probability profiles for the 3 state process, good and poor trackers are not significantly different, but exhibited some difference mainly due to transitions out of state 2.
In this section we analyse the WPB violation data and investigate the times to the third violation given times to second violation for both poor trackers and good trackers where tracking status is defined by WPB diagnostics. The process can be thought to have three states. State 1 corresponds to less than two violations, state 2 means two violations and if a patient is in state 3 then three violations have occurred. This is a sequential three state process shown schematically in Figure 17. Events of interest are a transition from state 2 to state 3, i.e. the occurrence of a third violation.
States of a patient’s agitation (violations) defined by certain levels of violations or jumps outside of the patient’s WPB bands - a 3 state process.
Time one is the patient’s entry time into state 2 and time two is the patients exit time from state 2 (i.e., entry time to state 3, so-called ‘death’ state). Time one is the patient’s entry time into state 2 and time two is the patient’s exit time from state 2 (i.e., entry time to state 3, so-called ‘death’ state).
In the case of multiple events of interest, the process can be treated as a Markov chain. Let Nij (t) be the process counting the number of observed transitions from state i to state j in the interval [0, t]. The transition intensity from state i to state j at time t is then λij (t) and gives the instantaneous risk of transition from state i to j.
Nij (t) has intensity process of the form λij(t)Ni(t) where Yi(t) is the number of individuals in state i just before time t. This is the setup of Simon and Makuch [37] who considered 4 states and two transitions of interest.
The concept of time in our ICU application represents time on-study (i.e. time at ICU) rather than calendar time. In the case of our 3 state process (Figure 8) the hazard functions of the two transitions of interest are λ13(t) and λ23(t) and the number of individuals in the states just before t are N1(t) and N2 (t), respectively. A chi squared test is conducted to test for independence between response and non-response as in the development formulated in [37].
This same test can be conducted to assess the association between strata and hazard rate. If λ23(0)(t) is the hazard rate (from state 2 to 3) for the good trackers and λ23(1) is the hazard rate (from state 2 to 3) for the poor tackers. Since the focus here is to test the effect of response (prior 2nd violation) on the hazard function, the null hypothesis of interest is H0: λ23(0) (t) = λ23(1). The hypothesis is tested via a log-rank type test following [37] which tests for the time-to-response bias. Now the equivalent of Table 2 in Simon and Makuch [37] can be constructed for both the good trackers and the poor trackers. Let N1(t) be the number of patients in state 1 at time t, and let N2 (t) denote the number of patients in state 2 at time t in Table 13.
Table 13 presents the WPB data as state-specific patient counts for each event time t.
Time | Events | ||
---|---|---|---|
3 | 12 | 12 | 7 |
4 | 7 | 10 | 5 |
6 | 6 | 6 | 2 |
9 | 5 | 5 | 1 |
12 | 4 | 5 | 1 |
18 | 2 | 6 | 1 |
19 | 2 | 5 | 1 |
26 | 0 | 6 | 1 |
27 | 0 | 5 | 1 |
28 | 0 | 4 | 1 |
33 | 0 | 3 | 1 |
36 | 0 | 2 | 1 |
43 | 0 | 1 | 1 |
(a) Good trackers. |
Time | Events | ||
---|---|---|---|
3 | 9 | 4 | 3 |
4 | 8 | 2 | 1 |
5 | 5 | 4 | 2 |
6 | 3 | 4 | 2 |
8 | 1 | 4 | 2 |
9 | 1 | 2 | 1 |
12 | 1 | 1 | 1 |
23 | 0 | 1 | 1 |
(b) Poor trackers |
Simon and Makuch’s [37] represeantation and formulation of the WPB data.
Mathematically the cumulative hazard function is conventionally estimated instead of the hazard function λ(t), as the latter is difficult to estimate. The cumulative hazard function and survival function is then given as,
Note that dij(u) above are the so-called end-state “deaths” i.e., third violations, the number of transitions from state i to state j in time interval [x, t]. The estimated survival and cumulative hazard curves are shown as in Figure 18.
Survival function of time to 3rd violation given the 2nd violation for good tracker and poor (non-)trackers (LHS) and cumulative hazard functions (RHS).
Survival curves and cumulative hazard functions were calculated according to Simon and Makuch’s method [37]. In essence, this counting process formulation keeps track of the number of patients in state 1 and 2 and event times (i.e., transitions into state 3). The survival package is used for estimation, where two times are used. Time one is the patients entry time into state 2 and time two is the patients exit time from state 2 (i.e. entry time to state 3, ‘death’).
The log rank test for H0: λ23(0) (t) = λ23(1), based on the counting process which utilises the number of individuals in the states just before t, these are, N1(t) and N2 (t), was performed. Accordingly, it is shown that the good tracker and poor tracker hazard rates/ (survival curves) time to the 3rd violation, given a 2nd violation has occurred, are statistically significantly different (p-value = 0.044), see left hand side of Figure 18. Notably, the hazard rate for the poor trackers is 2.1 times that of good trackers, 95% confidence interval (CI) [1.01, 4.38].
Further interpretation of the hazard function can be made by assessing the slope of the cumulative hazard function. Figure 18 (RHS), shows that the cumulative hazard increases faster for the poor trackers than the good trackers indicated by a much steeper slope. This suggests it takes less time for the poor trackers to reach their third violation than for the good trackers, this is also confirmed by the 95% confidence bands for the survival curves shown in Figure 19 for the good tracker and poor trackers. Note that the interpretation of Kaplan–Meier curves here is not as straight-forward as for conventional survival analysis. In our ICU A-S process formulation the curves do not correspond to fixed cohorts, as patients can contribute to different states/curves at different times (Table 13). Thereby the curves may be considered to represent hypothetical cohorts whose values remain constant after follow-up [38, 39].
Survival curves (95% CIs) for good (left) and poor (right) (non-)trackers.
A Cox proportional hazards model (CPHM) was then fitted with tracking status and a patient’s number of violations as covariates. The general CPHM hazard function is,
In our application we model two covariates: X1 (0 for a good tracker, 1 for a poor tracker), and X2 being the patient’s total number of violations in the CPHM. The log rank test associated with the CPHM confirmed that the good tracker and poor tracker hazard rates, and the survival curves were significantly different (p-value = 0.0496), with the hazard rate for the poor trackers being 2.1 times that of good trackers, with a 95% confidence interval of [1.01, 4.38]. The associated hazard rate for poor trackers is shown to be 1.87 times that of good trackers, with a 95% confidence interval [0.75, 4.70]. By inclusion of the total number of second time violations the effect of tracking status has only reduced slightly, and it remains significant (1.87 versus 2.1).
Log-rank tests were likewise conducted to assess times to different violation counts. Let VX denote the violation times for the Xth violation and the DX’s the associated event indicators (0 censored, 1 event of interest). Log rank tests for the two WPB tracking strata for selected violation times (X = 5, 10, 15, 20, 25, 30) showed significant differences between good and poor WPB trackers regarding the time to the patient’s time to 10th violation (p = 0.027), their 15th (p = 0.025) and their 25th violation (p = 0.011). Likewise, significance at the 10% level was demonstrated for times to the patient’s 5th, 20th and 30th violation (non-violatory lifetimes). All survival curves (not shown here) are significantly different or are close to being significant at the 5% level of significance. This confirms that the difference in time to violations between the good and poor trackers are consistently different, for these varying number of violations (VX, for X = 5, 10, 15, 20, 25, 30).
The time to a patient’s last violation event was also investigated using log-rank tests and Kaplan–Meier curves. We examined nine levels of the effect of the following covariate, which categorises the counts the patient levels of violations as follows: 0–5 violations, 5–10, 10–15, 15–20, 20–25, 25–30, 30–40, 40–50 and >50 violations. A histogram of the time to a patient’s last violation with boxplots of the times for each of these nine levels of categorisation shown is given in Figure 20 (the number above the boxplots gives the number of patients in each of the nine categories).
Histogram and boxplots of time to last violation. The numbers above the boxplot (RHS) specify the number of patients in each violation level.
Using the patient’s time to their final violation/jump, as the event of interest, and implementing log-rank based tests using this covariate adjustment, the log rank test demonstrated a statistically significant difference between the survival curves of time to last violation (p-value <0.000001) across the above nine different total number of violation levels, {0–5, 5–10, 10–15, 15–20, 20–25, 25–30, 30–40, 40–50, >50 violations}.
Notably, the levels that most contribute to the difference between trackers and non-trackers are those patients who have a total number of violations between 10 and 15, between 20 and 25 and >50, in that order. A log rank test on time to last violation (time of last jump outside the WPB bands) as the outcome of interest by tracking status, also establishes that there is a difference between the two survival curves (p-value = 0.045) (Figure 21). Clearly the WPB-based poor trackers tend to take longer to reach their last violation than good trackers. Corresponding Kaplan–Meier estimated curves are given in Figure 21. We note that up to ICU time 64 (≤64), 40% of the good versus 70% of the poor trackers are still violating, whereas after time point, 130, the corresponding percentages violating are 15% versus 40%, of the good versus poor trackers (Figure 21).
Estimated survival curves for time to last violation by tracking status.
A log-rank test from the counting process formulation [37, 38, 39] established a significant difference between the hazard curves of the WPB-based good and poor trackers (p-value = 0.044). Similarly log-rank tests performed for a variety of violation numbers to test for differences between good and poor trackers times to their 5th, 10th, 15th, 20th, 25th and 30th violation, showed evidence of a significant difference between good and poor trackers for a selection of these violation times (namely patient’s time to their 10th, 15th and 25th violation). In regard to analysing the patients time to their last recorded violation, log-rank tests and Kaplan–Meier curves showed that poor trackers tend to have a higher probability of still violating as time progresses in ICU compared to good trackers (p-value = 0.045).
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. 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In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. 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Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null},{id:"255360",title:"Dr.",name:"Usama",middleName:null,surname:"Ahmad",slug:"usama-ahmad",fullName:"Usama Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255360/images/system/255360.png",biography:"Dr. Usama Ahmad holds a specialization in Pharmaceutics from Amity University, Lucknow, India. He received his Ph.D. from Integral University, Lucknow, India, with his work titled ‘Development and evaluation of silymarin nanoformulation for hepatic carcinoma’. Currently, he is an Assistant Professor of Pharmaceutics, at the Faculty of Pharmacy, Integral University. He has been teaching PharmD, BPharm, and MPharm students and conducting research in the novel drug delivery domain. From 2013 to 2014 he worked on a research project funded by SERB-DST, Government of India. He has a rich publication record with more than twenty-four original journal articles, two edited books, four book chapters, and several scientific articles to his credit. He is a member of the American Association for Cancer Research, the International Association for the Study of Lung Cancer, and the British Society for Nanomedicine. Dr. Ahmad’s research focus is on the development of nanoformulations to facilitate the delivery of drugs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"333824",title:"Dr.",name:"Ahmad Farouk",middleName:null,surname:"Musa",slug:"ahmad-farouk-musa",fullName:"Ahmad Farouk Musa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333824/images/22684_n.jpg",biography:"Dato’ Dr Ahmad Farouk Musa\nMD, MMED (Surgery) (Mal), Fellowship in Cardiothoracic Surgery (Monash Health, Aust), Graduate Certificate in Higher Education (Aust), Academy of Medicine (Mal)\n\n\n\nDato’ Dr Ahmad Farouk Musa obtained his Doctor of Medicine from USM in 1992. He then obtained his Master of Medicine in Surgery from the same university in the year 2000 before subspecialising in Cardiothoracic Surgery at Institut Jantung Negara (IJN), Kuala Lumpur from 2002 until 2005. He then completed his Fellowship in Cardiothoracic Surgery at Monash Health, Melbourne, Australia in 2008. He has served in the Malaysian army as a Medical Officer with the rank of Captain upon completing his Internship before joining USM as a trainee lecturer. He is now serving as an academic and researcher at Monash University Malaysia. He is a life-member of the Malaysian Association of Thoracic & Cardiovascular Surgery (MATCVS) and a committee member of the MATCVS Database. He is also a life-member of the College of Surgeons, Academy of Medicine of Malaysia; a life-member of Malaysian Medical Association (MMA), and a life-member of Islamic Medical Association of Malaysia (IMAM). Recently he was appointed as an Interim Chairperson of Examination & Assessment Subcommittee of the UiTM-IJN Cardiothoracic Surgery Postgraduate Program. As an academic, he has published numerous research papers and book chapters. He has also been appointed to review many scientific manuscripts by established journals such as the British Medical Journal (BMJ). He has presented his research works at numerous local and international conferences such as the European Association for Cardiothoracic Surgery (EACTS) and the European Society of Cardiovascular Surgery (ESCVS), to name a few. He has also won many awards for his research presentations at meetings and conferences like the prestigious International Invention, Innovation & Technology Exhibition (ITEX); Design, Research and Innovation Exhibition, the National Conference on Medical Sciences and the Annual Scientific Meetings of the Malaysian Association for Thoracic and Cardiovascular Surgery. He was awarded the Darjah Setia Pangkuan Negeri (DSPN) by the Governor of Penang in July, 2015.",institutionString:null,institution:{name:"Monash University Malaysia",country:{name:"Malaysia"}}},{id:"30568",title:"Prof.",name:"Madhu",middleName:null,surname:"Khullar",slug:"madhu-khullar",fullName:"Madhu Khullar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/30568/images/system/30568.jpg",biography:"Dr. Madhu Khullar is a Professor of Experimental Medicine and Biotechnology at the Post Graduate Institute of Medical Education and Research, Chandigarh, India. She completed her Post Doctorate in hypertension research at the Henry Ford Hospital, Detroit, USA in 1985. She is an editor and reviewer of several international journals, and a fellow and member of several cardiovascular research societies. Dr. Khullar has a keen research interest in genetics of hypertension, and is currently studying pharmacogenetics of hypertension.",institutionString:"Post Graduate Institute of Medical Education and Research",institution:{name:"Post Graduate Institute of Medical Education and Research",country:{name:"India"}}},{id:"223233",title:"Prof.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/223233/images/system/223233.png",biography:"Xianquan Zhan received his MD and Ph.D. in Preventive Medicine at West China University of Medical Sciences. He received his post-doctoral training in oncology and cancer proteomics at the Central South University, China, and the University of Tennessee Health Science Center (UTHSC), USA. He worked at UTHSC and the Cleveland Clinic in 2001–2012 and achieved the rank of associate professor at UTHSC. Currently, he is a full professor at Central South University and Shandong First Medical University, and an advisor to MS/PhD students and postdoctoral fellows. He is also a fellow of the Royal Society of Medicine and European Association for Predictive Preventive Personalized Medicine (EPMA), a national representative of EPMA, and a member of the American Society of Clinical Oncology (ASCO) and the American Association for the Advancement of Sciences (AAAS). He is also the editor in chief of International Journal of Chronic Diseases & Therapy, an associate editor of EPMA Journal, Frontiers in Endocrinology, and BMC Medical Genomics, and a guest editor of Mass Spectrometry Reviews, Frontiers in Endocrinology, EPMA Journal, and Oxidative Medicine and Cellular Longevity. He has published more than 148 articles, 28 book chapters, 6 books, and 2 US patents in the field of clinical proteomics and biomarkers.",institutionString:"Shandong First Medical University",institution:{name:"Affiliated Hospital of Shandong Academy of Medical Sciences",country:{name:"China"}}}]}},subseries:{item:{id:"6",type:"subseries",title:"Viral Infectious Diseases",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11402,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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